Robust Wavelet Regression With Automatic Boundary Correction
This thesis proposes different robust methods in an attempt to keep using the idea of PWR and LP\iVR even beyond the usual assumptions of such outliers, independent or correlated non Gaussian noises and random missing data. Therefore, this thesis is divided into three parts. The first part introduce...
| Main Author: | |
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| Format: | Thesis |
| Language: | English |
| Published: |
2012
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| Online Access: | http://eprints.usm.my/60760/ http://eprints.usm.my/60760/1/Pages%20from%20Alsaidi.pdf |
| _version_ | 1848884531915915264 |
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| author | Mohamed Altaher, Alsaidi Almahdi |
| author_facet | Mohamed Altaher, Alsaidi Almahdi |
| author_sort | Mohamed Altaher, Alsaidi Almahdi |
| building | USM Institutional Repository |
| collection | Online Access |
| description | This thesis proposes different robust methods in an attempt to keep using the idea of PWR and LP\iVR even beyond the usual assumptions of such outliers, independent or correlated non Gaussian noises and random missing data. Therefore, this thesis is divided into three parts. The first part introduces five different robust methodologies to extend the validity of PWR and LPWR to describe data contaminated with outliers and independent noises. The second part pays special exception when the noise structure is correlated. |
| first_indexed | 2025-11-15T19:08:12Z |
| format | Thesis |
| id | usm-60760 |
| institution | Universiti Sains Malaysia |
| institution_category | Local University |
| language | English |
| last_indexed | 2025-11-15T19:08:12Z |
| publishDate | 2012 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | usm-607602024-06-26T03:10:03Z http://eprints.usm.my/60760/ Robust Wavelet Regression With Automatic Boundary Correction Mohamed Altaher, Alsaidi Almahdi QA1-939 Mathematics This thesis proposes different robust methods in an attempt to keep using the idea of PWR and LP\iVR even beyond the usual assumptions of such outliers, independent or correlated non Gaussian noises and random missing data. Therefore, this thesis is divided into three parts. The first part introduces five different robust methodologies to extend the validity of PWR and LPWR to describe data contaminated with outliers and independent noises. The second part pays special exception when the noise structure is correlated. 2012-12 Thesis NonPeerReviewed application/pdf en http://eprints.usm.my/60760/1/Pages%20from%20Alsaidi.pdf Mohamed Altaher, Alsaidi Almahdi (2012) Robust Wavelet Regression With Automatic Boundary Correction. PhD thesis, Universiti Sains Malaysia. |
| spellingShingle | QA1-939 Mathematics Mohamed Altaher, Alsaidi Almahdi Robust Wavelet Regression With Automatic Boundary Correction |
| title | Robust Wavelet Regression With Automatic Boundary Correction |
| title_full | Robust Wavelet Regression With Automatic Boundary Correction |
| title_fullStr | Robust Wavelet Regression With Automatic Boundary Correction |
| title_full_unstemmed | Robust Wavelet Regression With Automatic Boundary Correction |
| title_short | Robust Wavelet Regression With Automatic Boundary Correction |
| title_sort | robust wavelet regression with automatic boundary correction |
| topic | QA1-939 Mathematics |
| url | http://eprints.usm.my/60760/ http://eprints.usm.my/60760/1/Pages%20from%20Alsaidi.pdf |